Hybrid GA–GWO Based PID Controller Optimization and Comparis

MATLAB code comparing PID tuning using Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and a hybrid GA→GWO approach with performance and

This MATLAB project presents an intelligent approach to PID controller tuning using three different optimization methods:

1. Genetic Algorithm (GA)
2. Grey Wolf Optimizer (GWO)
3. Hybrid GA→GWO algorithm (uses GA for global exploration and GWO for fine-tuning)

The script automatically finds optimal PID gains (Kp, Ki, Kd) for a second-order plant model and compares system responses.

It evaluates each optimizer using an Integral of Absolute Error (IAE) performance index with a penalty for overshoot, ensuring both fast and stable control action.

Three simulation results are displayed:

• Figure 1: Step response comparison (GA vs GWO vs Hybrid GA→GWO)
• Figure 2: Disturbance rejection capability (Hybrid GA→GWO PID)
• Figure 3: Hardware-like discrete simulation (sampling, quantization, saturation)

This project is useful for students and researchers interested in control systems, evolutionary algorithms, and intelligent PID optimization.